The basal ganglia are a group of subcortical nuclei that contain about 100
million neurons in humans. Different modes of basal ganglia dysfunction lead to
Parkinson's disease and Huntington's disease, which have debilitating motor and
cognitive symptoms. However, despite intensive study, both the internal computational
mechanisms of the basal ganglia, and their contribution to normal brain
function, have been elusive. The goal of this thesis is to identify basic principles that
underlie basal ganglia function, with a focus on signal representation, computation,
dynamics, and plasticity.
This process begins with a review of two current hypotheses of normal basal
ganglia function, one being that they automatically select actions on the basis of
past reinforcement, and the other that they compress cortical signals that tend to
occur in conjunction with reinforcement. It is argued that a wide range of experimental
data are consistent with these mechanisms operating in series, and that in
this configuration, compression makes selection practical in natural environments.
Although experimental work is outside the present scope, an experimental means
of testing this proposal in the future is suggested.
The remainder of the thesis builds on Eliasmith & Anderson's Neural Engineering
Framework (NEF), which provides an integrated theoretical account of computation,
representation, and dynamics in large neural circuits. The NEF provides
considerable insight into basal ganglia function, but its explanatory power is potentially
limited by two assumptions that the basal ganglia violate. First, like most
large-network models, the NEF assumes that neurons integrate multiple synaptic
inputs in a linear manner. However, synaptic integration in the basal ganglia is
nonlinear in several respects. Three modes of nonlinearity are examined, including
nonlinear interactions between dendritic branches, nonlinear integration within terminal
branches, and nonlinear conductance-current relationships. The first mode
is shown to affect neuron tuning. The other two modes are shown to enable alternative
computational mechanisms that facilitate learning, and make computation
more flexible, respectively.
Secondly, while the NEF assumes that the feedforward dynamics of individual
neurons are dominated by the dynamics of post-synaptic current, many basal
ganglia neurons also exhibit prominent spike-generation dynamics, including adaptation,
bursting, and hysterses. Of these, it is shown that the NEF theory of
network dynamics applies fairly directly to certain cases of firing-rate adaptation.
However, more complex dynamics, including nonlinear dynamics that are diverse
across a population, can be described using the NEF equations for representation.
In particular, a neuron's response can be characterized in terms of a more complex
function that extends over both present and past inputs. It is therefore straightforward
to apply NEF methods to interpret the effects of complex cell dynamics at
the network level.
The role of spike timing in basal ganglia function is also examined. Although
the basal ganglia have been interpreted in the past to perform computations on
the basis of mean firing rates (over windows of tens or hundreds of milliseconds)
it has recently become clear that patterns of spikes on finer timescales are also
functionally relevant. Past work has shown that precise spike times in sensory
systems contain stimulus-related information, but there has been little study of how post-synaptic neurons might use this information. It is shown that essentially any neuron can use this information to perform flexible computations, and that these
computations do not require spike timing that is very precise. As a consequence,
irregular and highly-variable firing patterns can drive behaviour with which they
have no detectable correlation.
Most of the projection neurons in the basal ganglia are inhibitory, and the effect
of one nucleus on another is classically interpreted as subtractive or divisive. Theoretically, very flexible computations can be performed within a projection if each
presynaptic neuron can both excite and inhibit its targets, but this is hardly ever
the case physiologically. However, it is shown here that equivalent computational flexibility is supported by inhibitory projections in the basal ganglia, as a simple consequence of inhibitory collaterals in the target nuclei.
Finally, the relationship between population coding and synaptic plasticity is
discussed. It is shown that Hebbian plasticity, in conjunction with lateral connections, determines both the dimension of the population code and the tuning of
neuron responses within the coded space. These results permit a straightforward
interpretation of the effects of synaptic plasticity on information processing at the
network level.
Together with the NEF, these new results provide a rich set of theoretical principles
through which the dominant physiological factors that affect basal ganglia
function can be more clearly understood.
| Identifer | oai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/4179 |
| Date | January 2008 |
| Creators | Tripp, Bryan |
| Source Sets | University of Waterloo Electronic Theses Repository |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
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